# Privacy Preserved Federated Learning with Attention-Based Aggregation for Biometric Recognition

arXiv: https://arxiv.org/abs/2510.01113 · HTML: https://mlsanity.com/paper/2510.01113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Communication Efficiency in Federated Learning: Achievements and Challenges](https://mlsanity.com/paper/2107.10996/) (2021)
- [Federated Learning for Face Recognition with Gradient Correction](https://mlsanity.com/paper/2112.07246/) (2021)
- [Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters](https://mlsanity.com/paper/2201.12467/) (2022)
- [From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security](https://mlsanity.com/paper/2010.09258/) (2020)
- [Dynamic Attention-based Communication-Efficient Federated Learning](https://mlsanity.com/paper/2108.05765/) (2021)
- [FedFormer: Contextual Federation with Attention in Reinforcement Learning](https://mlsanity.com/paper/2205.13697/) (2022)

## Abstract (arXiv)

Because biometric data is sensitive, centralized training poses a privacy risk, even though biometric recognition is essential for contemporary applications. Federated learning (FL), which permits decentralized training, provides a privacy-preserving substitute. Conventional FL, however, has trouble with interpretability and heterogeneous data (non-IID). In order to handle non-IID biometric data, this framework adds an attention mechanism at the central server that weights local model updates according to their significance. Differential privacy and secure update protocols safeguard data while preserving accuracy. The A3-FL framework is evaluated in this study using FVC2004 fingerprint data, with each client's features extracted using a Siamese Convolutional Neural Network (Siamese-CNN). By dynamically modifying client contributions, the attention mechanism increases the accuracy of the global model.The accuracy, convergence speed, and robustness of the A3-FL framework are superior to those of standard FL (FedAvg) and static baselines, according to experimental evaluations using fingerprint data (FVC2004). The accuracy of the attention-based approach was 0.8413, while FedAvg, Local-only, and Centralized approaches were 0.8164, 0.7664, and 0.7997, respectively. Accuracy stayed high at 0.8330 even with differential privacy. A scalable and privacy-sensitive biometric system for secure and effective recognition in dispersed environments is presented in this work.